dimod.BinaryQuadraticModel.from_ising

classmethod BinaryQuadraticModel.from_ising(h, J, offset=0.0)[source]

Create a binary quadratic model from an Ising problem.

Parameters:
  • h (dict/list) – Linear biases of the Ising problem. If a dict, should be of the form {v: bias, …} where v is a spin-valued variable and bias is its associated bias. If a list, it is treated as a list of biases where the indices are the variable labels.
  • J (dict[(variable, variable), bias]) – Quadratic biases of the Ising problem.
  • offset (optional, default=0.0) – Constant offset applied to the model.
Returns:

Binary quadratic model with vartype set to Vartype.SPIN.

Return type:

BinaryQuadraticModel

Examples

This example creates a binary quadratic model from an Ising problem.

>>> import dimod
>>> h = {1: 1, 2: 2, 3: 3, 4: 4}
>>> J = {(1, 2): 12, (1, 3): 13, (1, 4): 14,
...      (2, 3): 23, (2, 4): 24,
...      (3, 4): 34}
>>> model = dimod.BinaryQuadraticModel.from_ising(h, J, offset = 0.0)
>>> model      # doctest: +SKIP
BinaryQuadraticModel({1: 1, 2: 2, 3: 3, 4: 4}, {(1, 2): 12, (1, 3): 13, (1, 4): 14, (2, 3): 23, (3, 4): 34, (2, 4): 24}, 0.0, Vartype.SPIN)